Latest / From First Principles / Octopus Camouflage, Orcas vs. Sharks, Civet Coffee & Sub-Diffraction Telescope Tech (EP. 16)

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Octopus Camouflage, Orcas vs. Sharks, Civet Coffee & Sub-Diffraction Telescope Tech (EP. 16) · From First Principles

• 0:00
  Wagwan Internet, this is your captain speaking, Lester Nari, joined as always by my co-host and our resident PhD, Krishna Chowdhury.
• 0:08
  We have a fantastic super episode this week with four stories.
• 0:13
  Quick breakdown, we're going to start out with, we may have figured out how to mass produce the camouflage that octopi use in their predatory adventures.
• 0:24
  This is out of UCSD.
• 0:30
  There's a little bit of beef happening between orcas and sharks.
• 0:34
  Yeah.
• 0:35
  And the paper sort of understands or breaks down this new hunting technique that orcas are participating in.
• 0:42
  That one's out of an institution we'll talk about in a second.
• 0:45
  The third story out of the University of Canada is about some shit.
• 0:51
  Yeah.
• 0:51
  They're going to start charging $80 for you to drink at Starbucks.
• 0:55
  Yeah, yeah, yeah.
• 0:55
  I'm not being facetious.
• 0:57
  It's coming to Beverly Hills Starbucks.
• 0:59
  Civet coffee.
• 1:00
  Yeah.
• 1:01
  Civet shitted coffee.
• 1:03
  Yeah.
• 1:04
  Again, not a joke.
• 1:05
  Not a joke.
• 1:06
  And we'll wrap it up with a fascinating story at UCLA where we...
• 1:12
  I'm not saying we're defying physics, but there's some new technology that we're using that makes our telescope see what we didn't think was possible.
• 1:21
  That's right.
• 1:21
  This is going to be a great episode.
• 1:23
  As always, this is From First Principles.
• 1:42
  My friend.
• 1:43
  How's it going?
• 1:44
  How are you?
• 1:45
  Welcome back.
• 1:46
  Yep.
• 1:47
  Thank you.
• 1:48
  We have a couple of housekeeping items.
• 1:51
  So number one, the engagement continues to be unbelievable.
• 1:55
  If you're watching this on YouTube, which is probably one of the only ways you're hearing me say this part of the show,
• 2:00
  There's going to be that little bell icon where you can subscribe.
• 2:03
  It's hugely helpful.
• 2:04
  We need to get the billionaires to get us in the algorithm.
• 2:08
  So if you hit that subscribe button, that's going to really help get this science education podcast to more people.
• 2:13
  We also are going to have our new website up soon, which will have all of the research paper links, all of the episode links.
• 2:20
  We may be launching some leaderboards.
• 2:23
  We'll see.
• 2:24
  But we're going to get that ready for the holiday season.
• 2:26
  So keep an eye out for that.
• 2:29
  But we're going to dive in and start with our first story.
• 2:33
  Yep.
• 2:34
  Out of UCSD, which is a biosynthesis story.
• 2:37
  Yes.
• 2:38
  About mass producing...
• 2:42
  What octopi or octopus use for camouflage, and this was out of nature biotechnology.
• 2:48
  Yeah.
• 2:49
  A lot of people love talking about, like, octopi are super intelligent.
• 2:53
  Yes.
• 2:54
  And everyone's like, oh, that's like an alien intelligence in the ocean.
• 2:56
  Yeah.
• 2:57
  And this has, I think, been something we've known about, but have just now made this next step of, like, how can we replicate?
• 3:03
  So let's talk about this story.
• 3:04
  Exactly.
• 3:04
  Yeah, yeah, yeah.
• 3:05
  The idea is...
• 3:06
  that we want to mass produce something called xanthomatin.
• 3:11
  Okay.
• 3:11
  It's nature's master of disguise.
• 3:14
  It's a pigment that is responsible for cephalopod camouflage.
• 3:19
  Okay.
• 3:20
  Cephalopods are your octopus, squid, cuttlefish.
• 3:23
  There you've got a photo.
• 3:24
  I can't see the octopus.
• 3:25
  The octopus are incredible at camouflage.
• 3:28
  Oh, there's an octopus in that photo?
• 3:29
  Yeah.
• 3:30
  In that red circle, there's an octopus in there.
• 3:32
  That's incredible.
• 3:33
  Yeah, but it's not only matched the texture, but the exact color characteristics of the coral reef or the ocean floor that's behind it.
• 3:42
  Yeah.
• 3:42
  And it's honestly defying any reason how good it is.
• 3:48
  This same pigment is also found in the coloration of butterflies and dragonflies, in the eyes of flies.
• 3:56
  So it's a ubiquitous biopigment.
• 3:58
  It's got a really high value because it's optoelectric, which means it can change its color with a response to electrochemical potential.
• 4:06
  So that's how the octopus can sometimes become super vibrant and then also at the same time become this, they look like a rock, right?
• 4:14
  They can be like pulsing with like strobe lights and at the same time do all of these other things.
• 4:20
  If we can replicate this biosynthesis…
• 4:24
  Then we can go to next generation electronics, like electrochromic displays.
• 4:28
  The main thing that's nice about this is it's biocompatible.
• 4:31
  So, I mean, one thing that I was thinking was like, what if you had like color changing tattoos?
• 4:36
  Yeah.
• 4:36
  Where like if you flex, then like it like just changes.
• 4:39
  It's, you know.
• 4:40
  Yeah.
• 4:41
  Yeah.
• 4:41
  Like it's like Avatar, dude.
• 4:43
  You know?
• 4:43
  It is like Avatar.
• 4:44
  Yeah.
• 4:45
  Yeah.
• 4:45
  That's a great reference.
• 4:46
  So, so.
• 4:47
  It would be really cool, right, if we could make a bunch of this.
• 4:50
  So when you say it's biocompatible, it means that it could interact with our, as biological beings, our system directly.
• 4:58
  Yeah, and it's not like toxic, right?
• 5:01
  Got it.
• 5:01
  Because it's already a sort of organic compound.
• 5:03
  That makes sense.
• 5:05
  The other thing is like it could be used in cosmetics if you have like multifunctional ingredients or like health and skincare.
• 5:10
  It's very UV protectant, these coatings.
• 5:13
  So if we can mass produce a lot of it.
• 5:15
  That would be really helpful for all sorts of industries.
• 5:17
  The bottleneck is a supply challenge, okay?
• 5:20
  You can't just go into biology and try to extract it.
• 5:22
  Like, how many butterflies are you going to kill, right?
• 5:25
  Or like octopus.
• 5:26
  It's just not.
• 5:27
  It's low-yielding.
• 5:27
  It's labor-intensive.
• 5:28
  And it's not ethical, right?
• 5:30
  It's not like mining.
• 5:31
  Yes.
• 5:32
  Which you could argue is also not ethical.
• 5:33
  Yeah, exactly.
• 5:34
  For different reasons.
• 5:35
  Exactly.
• 5:36
  So this breakthrough here reports the first successful industrial gram-scale biosynthesis.
• 5:42
  Okay.
• 5:42
  Understood.
• 5:43
  Got it.
• 5:43
  Yep.
• 5:43
  And the way that they did this scaling and this production is actually what I think the story is about.
• 5:52
  The real science is about because the way that they produce this is kind of a plug and play type thing.
• 5:58
  pathway that you could use to then manufacture other challenging biochemicals the analogy that this makes me think of is kind of like how crisper like was a platform for lack of a better term that had multiple use cases or functions yeah so that you could use it with and so there's a kind of a similar platform level discovery happening here yeah it's not specific to just this yeah this is kind of like uh yeah exactly this is kind of like a proof of concept mm-hmm
• 6:26
  that it can be done, but the way that they did it is what I think is really, really cool.
• 6:31
  Okay.
• 6:31
  Because here's the core challenge, okay?
• 6:34
  Whenever you want to install, the xanthanatin comes from some genomic pathway, right?
• 6:40
  There's some biochemical pathway in the octopus that creates xanthanatin, okay?
• 6:45
  The standard approach is to take that gene or take that bunch of genes that all contribute to this, stick that into a bacteria,
• 6:53
  And then have the bacteria produce it, right?
• 6:55
  Yep.
• 6:55
  You install a foreign gene and that's what you do.
• 6:58
  Got it.
• 6:58
  The problem is there's a metabolic burden.
• 7:01
  Okay.
• 7:02
  Because...
• 7:03
  If you've got this new pathway that's taking up a lot of energy, it's taking up a lot of the ATP, a lot of the carbon atoms even, and it confers no survival advantage, the bacteria doesn't want to do it.
• 7:16
  Right?
• 7:18
  Kind of sounds a little bit like the AI thing happening right now.
• 7:20
  Yeah, yeah.
• 7:21
  Exactly.
• 7:22
  Exactly.
• 7:23
  And, you know, you can actually, in terms of AI, you can use an analogy where it's like...
• 7:28
  All of the energy budget and the carbon budget of an organism is very strict.
• 7:35
  It's had billions of years of evolution to figure out this carbon atom has to go there, this carbon atom has to go there.
• 7:42
  This amount of ATP needs to be used for this and so on and so forth.
• 7:46
  It's had billions of years of evolution to figure that out.
• 7:49
  And now all of a sudden you install on this electrical grid like a giant server farm with no advantage.
• 7:56
  Right.
• 7:58
  The bacteria is not going to reroute all of its electricity, all of its energy, to make this server farm when it makes no sense, right?
• 8:06
  Right.
• 8:07
  In fact, if you have a bacterial population...
• 8:10
  You've got an evolutionary conflict because let's say a bunch of these bacteria are there.
• 8:14
  There's some mutation that cuts off supply to that server farm.
• 8:18
  That bacteria is going to grow faster because it's not wasting all that energy.
• 8:22
  And then in a population, it's kind of like antibiotic resistance where you've got a population that sort of...
• 8:30
  there's some bacteria that use up the resistance and actually are resistant to the antibiotic and some that aren't, right?
• 8:38
  And those are the ones that are going to go ahead.
• 8:41
  But in this case, it's like those that have budgeted well and have cut off this useless server farm are going to grow faster and then they're going to dominate the entire population.
• 8:48
  That makes total sense.
• 8:49
  Right?
• 8:50
  So that's the problem.
• 8:51
  It's like we're going against evolution.
• 8:53
  We're trying to engineer something, but there's all these cheaters around that are just going to do what they want to do.
• 8:59
  Because we're trying to use literal biology as the platform by which we generate this organic compound.
• 9:07
  Yeah.
• 9:07
  Like we're not using machines.
• 9:09
  No.
• 9:10
  And a factory.
• 9:11
  Yeah.
• 9:11
  It's we're literally using biology.
• 9:13
  Yeah.
• 9:14
  And there are these inherent properties underlying the evolutionary process of biology.
• 9:19
  Yeah.
• 9:19
  That are preventative factors from just being like maximize the output we care about.
• 9:23
  Because it's not purely create.
• 9:24
  The system is not purely materialized.
• 9:26
  controlled by like human engineering input exactly yeah yeah so so we need to figure out a way to to make the bacteria do what we want to do right okay and run the server farm that like gives us and like makes us um have this octopus superpower right
• 9:46
  And the strategy that they're using is this plug-and-play strategy.
• 9:50
  It came out of Nature Biotech.
• 9:52
  Growth-coupled biosynthesis.
• 9:54
  That's what they're calling it.
• 9:56
  What they're doing is actually quite a simple thing.
• 10:00
  They're creating a fitness advantage.
• 10:01
  They're creating an evolutionary advantage for those that are using the server farm.
• 10:06
  And what they're doing is...
• 10:08
  They've created what's called an oxotroph.
• 10:10
  It's an organism that has, through some kind of mutation, lost the ability to synthesize some essential ingredient.
• 10:17
  Okay.
• 10:18
  Okay?
• 10:18
  And the only way it can get this essential ingredient is if it runs the server farm.
• 10:24
  It runs this mechanical, biomechanical pathway that we've installed that gives it that ingredient to let it grow.
• 10:31
  That's that's so it's like like if the if if bacteria had ethics, this would be very.
• 10:36
  Yeah.
• 10:38
  Yeah.
• 10:40
  And I'm sure there's people out there that are making that argument.
• 10:42
  And I mean, I don't I don't have good arguments about that.
• 10:45
  OK, that's that's not my that's not the scope of this.
• 10:47
  Yeah, this episode of Oxitrophs.
• 10:51
  Yeah, but it's pretty insane.
• 10:53
  It's called synthetic oxotrophy, okay?
• 10:55
  So the idea is you have a pathway, right?
• 10:58
  And on the bottom, you've got your central pathway that grows the cell, that does metabolism and all that other stuff.
• 11:06
  And usually the input comes in.
• 11:08
  The input comes in from this side, and then it can fork into two paths.
• 11:12
  You've got, let's say, a bunch of carbon atoms.
• 11:14
  The carbon atoms can go to make more bacteria, or it can go to do this...
• 11:19
  Xanthomatin production, this engineered production.
• 11:24
  If you've got a choice, the bacteria is going to always pick grow myself.
• 11:28
  Evolution dictates that.
• 11:30
  But if it doesn't have a choice, it has to go through the server farm in order to get the carbon to grow itself.
• 11:36
  Then actually those that are better at doing that artificial production.
• 11:42
  pathway are going to have an evolutionary advantage.
• 11:45
  We've actually used evolution to our advantage now by creating a foreign Xanthamatin production pathway that...
• 11:54
  puts out a byproduct that it needs.
• 11:56
  This is interesting.
• 11:58
  We've hijacked our understanding of evolution to know that we need to create basically a need for the system to go down the path we want it to in order for it to be more evolutionarily advantaged against others.
• 12:12
  And that it's a little bit, it's interesting.
• 12:17
  It's interesting, dude.
• 12:18
  At least it's bacteria, man.
• 12:19
  At least...
• 12:20
  Let's hope they don't scale it up to larger eukaryotes.
• 12:23
  Yeah, exactly.
• 12:24
  But anyway.
• 12:26
  Yeah, so they're using this bacteria called Pseudomonas putida.
• 12:31
  It's a soil bacterium.
• 12:34
  It's got a robust chassis that has a lot of metabolic versatility, meaning that it can eat a bunch of different things.
• 12:41
  It's not like, you know, there's not one pathway that it can go through.
• 12:45
  It's not vegan.
• 12:45
  Yeah.
• 12:45
  Yeah, yeah, yeah, yeah.
• 12:47
  Even though it's like mostly glucose, it can like eat glucose in multiple different ways.
• 12:51
  Okay.
• 12:52
  And good natural tolerance to substrates and products, like anything that's coming out of that factory that we just installed.
• 13:00
  Yes.
• 13:01
  It's got a good tolerance for all sorts of stuff.
• 13:04
  So they're using this soil bacterium.
• 13:06
  And what they're doing is they're taking a molecule as hostage.
• 13:09
  It's the 510 MTHF.
• 13:13
  It's basically a C1 carrier.
• 13:16
  It's a carbon carrier for DNA and RNA.
• 13:19
  Mm-hmm.
• 13:20
  And for proteins, it can't make methionine without this thing.
• 13:23
  And it can't make the A and the G that's in our DNA.
• 13:27
  Okay.
• 13:27
  So without this pathway, it can no longer grow because it can't make DNA and it can't make proteins.
• 13:35
  We're creating almost this like artificial famine at the cell level.
• 13:39
  Exactly.
• 13:40
  And requiring it to go through this pathway that will generate an output we care about in order to be able to literally feed itself.
• 13:46
  Feed itself.
• 13:47
  Yeah, yeah.
• 13:48
  And then comes the production line.
• 13:50
  So now how does it actually make the food that it needs, right?
• 13:52
  What we do is we put a bunch of genes on a plasmid.
• 13:56
  A plasmid is a circular bit of DNA that we can use to...
• 14:02
  what the bacteria is producing.
• 14:03
  It's just foreign DNA that we can plug in.
• 14:06
  Now, these bacteria are already stressed out because they can't eat, so they're just trying to take in whatever help they can.
• 14:13
  We put in this plasmid that has genes on the plasmid to...
• 14:18
  create this artificial pathway, okay?
• 14:21
  Where on its way to creating xanthanatin, what it's going to do is create formate, which is a precursor for tryptophan and all of the other things that the bacterian needs.
• 14:32
  Right, right, right.
• 14:33
  Right?
• 14:34
  And the ultimate goal is you've got like a sole carbon source.
• 14:38
  which is through the factory.
• 14:39
  Yeah, yeah, yeah.
• 14:41
  You have to go, you have to pay the cost to be the boss.
• 14:44
  You literally, you have to go kiss the ring to survive.
• 14:47
  To survive, yeah.
• 14:49
  And even then, it wasn't so simple, okay?
• 14:51
  Okay.
• 14:51
  Because you have that single carbon source, but the bacteria still, the initial bug was it still needed supplemental glycine and L-tryptophan, which is...
• 15:01
  to kind of expensive ingredients that you need to give the bacteria in order to survive.
• 15:06
  You can't just give it glucose.
• 15:08
  So then what they did was they had another evolutionary lab there.
• 15:13
  In their little lab.
• 15:14
  Okay.
• 15:15
  What you do is you say, okay, I need you to make glycine, right?
• 15:19
  I'm going to give you a bunch of glycine at first, and I'm going to slowly wean off your entire population of glycine.
• 15:25
  And you're hoping that the bacteria just learn through some mutations because of this pressure.
• 15:31
  They're going to learn how to make their own glycine.
• 15:33
  Yep.
• 15:34
  Either learn how to make their own glycine or live without it.
• 15:36
  Yep.
• 15:37
  Okay?
• 15:37
  And that's exactly what ended up happening.
• 15:39
  Yep.
• 15:41
  The bacteria had a single amino acid change in the met K gene.
• 15:46
  And then all of a sudden they didn't need glycine.
• 15:48
  Right.
• 15:48
  Supplemental glycine.
• 15:50
  You were sort of basically through this providing withdrawal mechanism.
• 15:55
  Mm-hmm.
• 15:56
  Yeah.
• 15:57
  driving evolutionary progress to basically fulfill the gap of the withdrawal of providing it.
• 16:05
  Yeah.
• 16:05
  Directly to...
• 16:06
  Interesting.
• 16:07
  And it worked.
• 16:07
  And it worked, right?
• 16:08
  It worked.
• 16:09
  And what's cool about this is like this entire pathway, right?
• 16:12
  You've got the factory that's going in.
• 16:14
  Yes.
• 16:15
  The factory that they're using, that they've put in through this plasma, through this foreign DNA, the stuff that they got from the octopus and whatever, that can't just be...
• 16:24
  the native factory, because it has to have a byproduct that encourages the bacteria to actually work.
• 16:30
  So it has to be leaky in some sense.
• 16:33
  Not all of the carbon can go into the xanthanatine production.
• 16:38
  Some of it has to go out in order to create formate and then become this sole carbon source.
• 16:44
  Yep, that makes sense.
• 16:45
  So the factory has sort of two functions, both creating what we care about and also providing enough for the cell to continue to propagate.
• 16:54
  Yeah, yeah, yeah.
• 16:55
  And then those that ran the factory more actually had an advantage because they're the ones that could actually grow.
• 17:00
  Yep.
• 17:01
  Yeah, it's pretty crazy.
• 17:02
  That's really crazy.
• 17:03
  It's really crazy.
• 17:04
  And one of the ways that they actually showed that this was happening is an old school technique where you take an isotope of an atom.
• 17:12
  In this case, instead of carbon-12, they used carbon-13.
• 17:15
  Okay.
• 17:16
  And they fed it glucose with carbon-13 in a specific part of the glucose molecule, second from left or whatever.
• 17:23
  Yeah.
• 17:25
  And you can trace, because this carbon is a 13 instead of a 12, it's a little bit heavier.
• 17:30
  So you can trace how this carbon 13 goes through that entire chemical pathway, right?
• 17:35
  Because some of those molecules are going to be a little bit heavier than the other ones.
• 17:40
  Mm-hmm.
• 17:41
  Because it's got one extra neutron.
• 17:44
  And what you can show is atom by atom, the carbon traveled along this xanthamatin pathway.
• 17:49
  So through the factory, you can see, okay, now it's on the floor.
• 17:52
  Now it's there.
• 17:53
  Now it's getting this.
• 17:55
  There's an indicator that allows us to see the through line of the entire process to know that we're actually routing through the factory.
• 18:02
  Yeah, yeah, yeah.
• 18:03
  And you can confirm that this is what's happening, right?
• 18:05
  There's nothing else that's weird.
• 18:07
  And then finally, you know, you get this optimized ePuma xanthamatin strain that in 72 hours, you get a bunch of your pigment, a thousand times more than what you were getting earlier, or a lot less work.
• 18:21
  I mean, obviously, a lot of work into doing this, but now that you can do it.
• 18:24
  Now that you've created the system.
• 18:25
  Yeah, 2.4 grams per liter of xanthamatin powder.
• 18:29
  So this is...
• 18:30
  This is, you know, it's not only going to transform, let's say, the camouflage and the cosmetics.
• 18:36
  Yes.
• 18:36
  But I think what to me, what really struck was the way in which they did this.
• 18:43
  Right.
• 18:44
  This biosynthetic.
• 18:46
  hijacking of the metabolism right we now have sort of this plug and play platform again i don't mean to bring up the crisper analogy again but it's it's similar in the sense that other research teams are going to be able to take the insight from how you can basically facilitate biosynthesis yeah by this sort of like uh this
• 19:08
  pathway that advantages those that go through the factory that produce what you want it to.
• 19:14
  And you can apply that to any number of other of biosynthetic products.
• 19:19
  outputs that you're looking for.
• 19:20
  Yeah, yeah, yeah.
• 19:21
  And like, that's... That's the key insight for me.
• 19:24
  No, which, obviously... Yeah, I think... And, you know, you can accelerate production of antibiotics, plant alkalides, polyketides, all sorts of... That's fascinating.
• 19:33
  ...biochemical things.
• 19:35
  And you can make the factory itself be different, right?
• 19:38
  Because all you need is something that releases a C1 molecule.
• 19:41
  It's a molecule with a single carbon.
• 19:43
  So it can be the formate pathway, the formaldehyde pathway...
• 19:47
  Who knows, even like CO2, you could hijack respiration itself in the mitochondria, right?
• 19:53
  It just leads to, I mean, that's probably a lot harder because there's so many more steps.
• 19:57
  But-
• 19:58
  Knowing that there's a stage one entry point to utilizing this as a methodology for biosynthesis means that there are any number of other use cases that with some amount of additional research are going to be unlocks.
• 20:13
  I mean, this is actually really, really cool.
• 20:15
  I thought it was really, really fascinating.
• 20:17
  Yeah.
• 20:18
  So we're going to be able to have makeup that's color changing.
• 20:23
  We're going to be able to have all your vehicle is going to be laced with octopi camouflage.
• 20:32
  So if you're driving at night and you want to go night mode.
• 20:34
  You have a plant-based display.
• 20:36
  I don't know.
• 20:36
  Like all sorts of stuff, dude.
• 20:38
  That's actually.
• 20:39
  Okay.
• 20:39
  So this is out of UCSD.
• 20:41
  They've now sort of created a platform for mass producing equipment.
• 20:46
  uh, octopus camouflage, but it is a platform that can now be extrapolated into other arenas.
• 20:51
  Yeah.
• 20:52
  Uh, this is a paper of nature biotechnology.
• 20:55
  Uh, that's a great, that's a great story.
• 20:57
  Yeah, that was cool.
• 20:58
  I thought that was really cool.
• 20:59
  Fascinating story.
• 21:00
  We're gonna move on to our story number two and our story number two.
• 21:04
  Now I'm going to, I'm going to do my best.
• 21:06
  Our story number two is out of an institution called the Protección y Conservación Palagasia Association Civil AC, as well as CSU Monterey Bay.
• 21:17
  Yes.
• 21:18
  And this is about the new hunting techniques of orcas.
• 21:20
  Like we said, there's a beef in the ocean, right?
• 21:24
  And so this research study is looking at these new hunting patterns of orcas and,
• 21:30
  And you thought this was interesting, and I'm curious why you put this in the show notes.
• 21:33
  Yeah.
• 21:34
  I want to know about – because we've seen the story about orcas attacking yachts in the Mediterranean or on the Amafi Coast, wherever.
• 21:43
  But it does seem like, again –
• 21:46
  we're in the ocean for two of our stories back to back, which is funny, but the intelligence aspect of this is interesting to me, but that's exactly right.
• 21:53
  Okay.
• 21:53
  That's exactly right.
• 21:54
  It's the intelligence aspect.
• 21:55
  That's interesting to me.
• 21:57
  And more than even the intelligence, it's,
• 22:00
  culture, I think, that we're getting at.
• 22:02
  Okay?
• 22:03
  And I'm going to get into that a little bit later, but that's the key word why I got interested.
• 22:08
  Because to me, I'm fascinated with human culture.
• 22:10
  Right.
• 22:11
  And it seems like there's a few papers that we're going to go through that talk about animal culture.
• 22:16
  Okay.
• 22:16
  And I find that just extremely fascinating.
• 22:18
  So the culture wars are not human-specific?
• 22:20
  No, no, no.
• 22:21
  In this case, they probably have a hegemonist culture.
• 22:25
  Compared to ours.
• 22:25
  But actually, no, that's not even true.
• 22:27
  Even the orcas have dialects among their populations, which is fascinating to think about.
• 22:32
  That's interesting.
• 22:33
  Right?
• 22:33
  That, like, different populations of orcas might not understand each other.
• 22:36
  If you're not on the side of the equator.
• 22:38
  Yeah, like the Pacific ones and the Atlantic ones are going to be like, why do you talk funny?
• 22:42
  He looks like me, but he talks really funny.
• 22:45
  You know?
• 22:46
  It's a groundbreaking paper, I think, from Higuera Rivas.
• 22:50
  Yeah.
• 22:51
  published in November this year, Frontiers in Marine Science.
• 22:56
  The idea is it's the first ever recorded orca predation on young great white sharks in Mexican waters off the coast of Baja, California.
• 23:05
  It's actually the second time that we've seen orcas attacking great whites.
• 23:09
  The first time was off the coast of South Africa.
• 23:11
  So it's pretty far away.
• 23:13
  But the technique is remarkably similar.
• 23:16
  Okay.
• 23:17
  Okay?
• 23:17
  Which is kind of weird.
• 23:18
  Meaning it transcends?
• 23:20
  It might be.
• 23:21
  Yeah, yeah.
• 23:22
  I'm not going to say that it does.
• 23:23
  And the paper does not claim that it does.
• 23:24
  Fair enough.
• 23:25
  But I just find it kind of funny.
• 23:26
  Fair enough.
• 23:27
  Right?
• 23:28
  But that's a tall order to make.
• 23:29
  But I will show later on that whales have done this.
• 23:33
  Okay.
• 23:33
  Okay?
• 23:34
  So, the first encounter was on August 15, 2020.
• 23:37
  They...
• 23:39
  Found a pot of five female orcas, one adult and four sub-adults.
• 23:43
  And what they did was the one orca pushes the shark up to the surface.
• 23:49
  Then another orca just like punches it.
• 23:52
  Okay.
• 23:53
  Causing bleeding.
• 23:54
  And then there's two others that swim around the shark to make it go upside down.
• 23:59
  And then once the shark is upside down, you get this thing called tonic immobility where the shark can't really move.
• 24:04
  Mm-hmm.
• 24:05
  And then while that shark can't really move, the other orcas then eat out its liver.
• 24:11
  And like they bring out its liver and that photo had the liver there.
• 24:14
  And then they share it with the kids.
• 24:17
  Oh, that are kind of waiting in the wings.
• 24:20
  Yeah, yeah, yeah.
• 24:21
  That have sort of been like a part of this, but not really.
• 24:23
  Right, right, right.
• 24:24
  Right?
• 24:25
  And then they had a second encounter two years later at the same exact spot, which suggests that it's predictable.
• 24:30
  It's seasonal hunting pattern.
• 24:32
  This time it was an adult male, adult female, two sub-adults, and a calf.
• 24:36
  And this time the kid did nothing, just like wait, until juvenile white shark again.
• 24:40
  Yeah.
• 24:41
  much smaller than the orca gets wrecked and then shark starts bleeding liver is exposed they eat the liver and only the liver because the liver is super nutrient rich and has a bunch of like you know bang for buck yeah and it's in per pound it's like it seems like they're trying to give it for the young yeah they're saving stuff for the young i thought that was really cool that's really interesting
• 25:03
  Yeah, it's a pretty cool toolkit.
• 25:05
  I mean, obviously they're on boats, but a lot of the video and a lot of the analysis comes from the video from DJI Phantom 4 Pro, which is one of those drones.
• 25:16
  So drone photography has really...
• 25:20
  revolutionized marine ecosystem behavioral science right and sightings because it looks like this is it was a multi-platform observation system there's surface aerial underwater and photo identification yeah yeah yeah it's a multimodal yeah yeah exactly exactly yeah yeah and and and the the trick that they're using is kind of interesting they're using they're
• 25:44
  people who go in the middle of sharks and they're like, nothing's going to happen because I'm going to poke its nose.
• 25:49
  I think a lot of times what they're trying to do is turn it upside down and then it just freezes.
• 25:54
  There's tricks, but it's cool that orcas have figured this out.
• 25:58
  Organically.
• 25:59
  Yeah, they're both apex predators, right?
• 26:01
  The juvenile sharks and the orcas.
• 26:05
  But because they're competing, one could argue that they're just trying to take out
• 26:10
  the other guy who's trying to compete for the same resources they're paying for the same food supply so exactly if you're eating my food like you're you're now my enemy yeah and these these orcas only go after juvenile white sharks i mean i was actually going to ask that because like clearly they're like going after the little ones yeah yeah because the older ones when they see an orca they just get the hell out of it they're like i'm
• 26:27
  out yeah because they i guess they know they they've got some learning capability and they don't return we've seen white sharks see an orca and then they don't return for like two or three years oh wow they'll just leave they're like i'm good and they're like oh so that's a bad neighborhood i'm not gonna spin the block here yeah yeah i'll find my seals elsewhere
• 26:48
  That's actually really interesting.
• 26:50
  Yeah.
• 26:51
  And so now, I mean, there's reports in South Africa where the original report happened.
• 26:55
  The seal population there has gone a little bit out of control because the great white sharks just, like, avoid that area.
• 27:02
  Because the orcas have kind of— Yeah, and the orcas, you know, they swim around all over the ocean.
• 27:05
  Right.
• 27:06
  And so there's not a constant supply of predatory pressure.
• 27:09
  If that makes sense.
• 27:10
  And the seals have kind of— Exploded.
• 27:13
  Yeah, yeah.
• 27:14
  Which is crazy to think about.
• 27:15
  And the reason why I was talking about this being something that reminded me of culture is because it reminded me of a study that came out a long time ago.
• 27:29
  There used to be...
• 27:30
  So culture is something that we know animals do.
• 27:33
  What does culture mean?
• 27:34
  Culture means that you've got effectively some type of strategy...
• 27:42
  That is conferred through behavior and through social constructs, like groups of individuals.
• 27:48
  It's not something that is innate.
• 27:50
  Right.
• 27:51
  It's something that's learned from your peers.
• 27:53
  Yep.
• 27:54
  So you get the benefit of their experience, right?
• 27:57
  Mm-hmm.
• 27:57
  Like...
• 27:58
  You don't have to live through an orca attack, for example, if you're a juvenile shark and you can learn through culture.
• 28:04
  I don't know if sharks can, but I'm just giving you the example here.
• 28:08
  You don't have to go through that firsthand experience.
• 28:11
  Yes.
• 28:11
  Right.
• 28:11
  Of like, oh, that was bad.
• 28:13
  Yes.
• 28:13
  No, your parents tell you that was bad.
• 28:15
  Don't do that.
• 28:15
  Sometimes we're just like, yeah, I'll try.
• 28:18
  But there's other things where like, OK, clearly that's like that.
• 28:21
  Right.
• 28:22
  So.
• 28:24
  So there's examples of this.
• 28:25
  For example, Shark Bay bottlenose dolphins.
• 28:28
  These are sponging techniques where they carry around a sponge, like one of those, like SpongeBob, like the natural sponges.
• 28:36
  And they use that to probe the seafloor and that protects their bottlenose.
• 28:41
  Right?
• 28:41
  When they're like foraging the seafloor.
• 28:43
  Yeah.
• 28:43
  Because it's almost like a mission.
• 28:45
  Yeah.
• 28:46
  And it's passed from mother to calf.
• 28:48
  So it's a vertical cultural transmission.
• 28:50
  Okay.
• 28:50
  Right?
• 28:50
  Yep.
• 28:50
  Yep.
• 28:51
  And then we've got Japanese macaques.
• 28:53
  They wash their sweet potatoes.
• 28:54
  A little macaque.
• 28:55
  Yeah, they wash their sweet potatoes before eating it.
• 28:58
  And that's something that's culturally passed down because like the youngsters see the elders doing it.
• 29:02
  They're like, okay, that's the way to eat this thing, right?
• 29:05
  Yep.
• 29:06
  So orcopods, they've shown to have cultural things.
• 29:10
  For example, complex, stable vocal dialects that I was talking about.
• 29:14
  Almost like languages where different geographic...
• 29:16
  populations will have different dialects and those are obviously learned socially there was one paper that is honestly one of my favorite papers um in animal behavior okay it was in 2021 out of the royal society what it did was analyze 19th century american whaler log books in the north pacific ocean okay okay and these whalers kept meticulous lab notes when they went out in the field to try to kill sperm whales okay love to see it
• 29:43
  And back then, I think sperm whales were used for all sorts of stuff.
• 29:46
  Not only their meat, but this was before electricity.
• 29:49
  Right.
• 29:49
  So all of the oil was being used to light up our streets.
• 29:52
  And that actually caused one of the big epidemics of near extinction for these whales all across the ocean.
• 30:00
  Right.
• 30:01
  And the process of whaling was as follows.
• 30:03
  In the 18th, 19th century, you'd go with your ship, which was...
• 30:09
  powered by a sail, you'd spot some whales, and then you'd go out down in these duffies and try to harpoon them, the whales.
• 30:17
  Yes.
• 30:17
  And then if you harpooned one, you'd bring it back to the ship for extraction.
• 30:20
  Yes.
• 30:20
  Okay.
• 30:22
  What they noticed was whenever they went to a certain spot, the success rate plummeted from when they first got there.
• 30:32
  Okay.
• 30:33
  Yeah, yeah, yeah.
• 30:34
  So they go to a certain spot.
• 30:35
  On the top, you've got a map of all of the spots that they've been in the North Pacific.
• 30:40
  This is between Japan and America.
• 30:42
  And there you've got the rate at which you're able to kill whales.
• 30:47
  Yes.
• 30:48
  Okay?
• 30:49
  Yes.
• 30:49
  And the rate was diminishing.
• 30:52
  Now, there's four hypotheses.
• 30:54
  Okay.
• 30:55
  Okay?
• 30:55
  There's four ways we can think about it.
• 30:57
  One is just the first guys who went to that particular spot were just good at it.
• 31:01
  They were LeBron and you're not LeBron.
• 31:02
  Yeah, yeah.
• 31:03
  And so everyone, that doesn't make sense because we have the logbooks from the same whalers that go to other spots like the North Atlantic and they get the baseline rate.
• 31:12
  The fact that this is new, right?
• 31:13
  This is the North Pacific.
• 31:15
  So this is a harvesting ground for whales that hasn't been extracted before, right?
• 31:20
  Whaling has been around for centuries in the North Atlantic, right?
• 31:23
  Europeans have been at it forever.
• 31:25
  Yes.
• 31:26
  So that's the first hypothesis.
• 31:27
  The first whalers were good.
• 31:28
  That's not the case.
• 31:29
  Yep.
• 31:30
  Second one is the first whales that were taken out were these juvenile sort of slower, let's say, or they weren't very good at getting away.
• 31:42
  That also is something that we can model, right?
• 31:45
  We can model, we can have some type of population model that says, okay, like the slower whales in a population die out.
• 31:52
  How would the rate decrease then if it's some sort of random strategy?
• 31:56
  Right.
• 31:56
  Okay.
• 31:56
  Okay.
• 31:57
  The third one is the whales learn from personal experience, meaning I'm part of a pod that had this crazy thing happen to me where a bunch of our pod got shot in the ocean and then, and then got extracted.
• 32:11
  And so now I'm never going to have that happen to my pod again.
• 32:14
  This is literally, I think the plot of the second or third avatar movie.
• 32:17
  Yeah.
• 32:18
  Yeah.
• 32:18
  One of those.
• 32:19
  Like it's one of them where the ocean thingies and then they were getting hunted and then they didn't go back to the spot.
• 32:24
  Exactly.
• 32:25
  Exactly.
• 32:25
  Because that's where we lost all our people.
• 32:28
  Exactly.
• 32:28
  And then the fourth one is the best one.
• 32:31
  Okay.
• 32:31
  That I want to be true.
• 32:33
  I'll just be honest.
• 32:33
  Okay.
• 32:34
  And it's that the whales learned between unit learning.
• 32:38
  Meaning, there was a pod that I ran into yesterday that had that experience.
• 32:43
  They told me about it, and they told me how to get away.
• 32:45
  The idea is you're on the block.
• 32:48
  Your block is block two.
• 32:50
  But the cats from block one pull up, and they're like, yo, yesterday on block one...
• 32:55
  This happened, so y'all watch out.
• 32:58
  Yeah.
• 32:58
  And so there's a transferring socially of learning without having to physically or literally experience.
• 33:05
  And that's the culture part.
• 33:07
  That's the culture part.
• 33:07
  That's the culture part, right?
• 33:09
  So the question is, do the whales have culture?
• 33:11
  Is the fourth hypothesis true?
• 33:13
  Yeah.
• 33:13
  or any of these.
• 33:14
  And what they did was modeling.
• 33:16
  They did mathematical modeling to see how that rate would decrease based on these three hypotheses.
• 33:21
  And the one that had the best fit was the one where the whales adapted, learned, and shared defensive knowledge
• 33:29
  Too rapidly for genetic evolution.
• 33:31
  The point is the rate of decrease of efficacy of the hunting was so steep that it couldn't have just been from personal experience because there wasn't enough volume for personal experience to be the trigger.
• 33:42
  There's not enough juveniles in the population spread of whales for it to just be the juveniles.
• 33:47
  And it can't be that they're just that good because in other areas they were doing just fine.
• 33:51
  And so these other three things don't account for the rate of decrease in efficacy.
• 33:59
  crazy that okay when you got these whales and here's what they would do um usually they do defensive huddling okay so if orcas come up yeah what you do is like kind of what elephants do where you huddle around your young and you try to bite the people around that slows you down and that is super effective ineffective against boats right you come up on a boat they're already slowed down great just you know go at it so
• 34:22
  Pretty soon they realized that's not going to work.
• 34:24
  And in their whaler journals, they showed that the whales abandoned that traditional defensive huddling and they went for new tactics.
• 34:30
  For example, if they know which way the wind is, just swim opposite direction of the wind.
• 34:35
  Oh, because they know we can't.
• 34:38
  They figured out that these guys can only go in the direction of the wind with their big ship.
• 34:45
  Right.
• 34:45
  And so they just like swim in the other direction.
• 34:48
  Evasive deep diving resurface like many miles away.
• 34:51
  Yep.
• 34:52
  Away from the ship.
• 34:53
  Yep.
• 34:54
  Right.
• 34:54
  And some people just started doing aggressive defensing.
• 34:57
  If like they were cornered, they would just like start attacking.
• 35:00
  You know, the best defense is the attack.
• 35:02
  Yeah.
• 35:02
  You know what's funny is the social learning from this era might still be persisting, which is why the yachts are getting attacked.
• 35:09
  Yes, I was going to say.
• 35:11
  And that's the final photo that we have.
• 35:13
  That's right.
• 35:14
  It's like now we've got this thing coming back where the orcas are now all around Spain and Portugal.
• 35:19
  They're just like attacking yachts, right?
• 35:22
  And it's become like kind of an epidemic of orca on yacht violence, right?
• 35:26
  Yep.
• 35:27
  Yep, and we don't have harpoons on those yachts.
• 35:29
  Yeah, and that also looks like it's a cultural thing.
• 35:31
  Because it started one day, and then everyone started doing it.
• 35:35
  All across the North Sea, all across Portugal, through the Straits of Gibraltar.
• 35:41
  I wouldn't be surprised if not all of the Mediterranean is starting it.
• 35:45
  And what's interesting is, at the time of when whaling was happening, you have sort of a reason for the behavior change that's identifiable.
• 35:53
  Yeah.
• 35:54
  It's not necessarily obvious or clear in this recent... Yeah, it's not necessarily obvious.
• 36:00
  There was one hypothesis that was saying that, like, oh, because we have, like, so much tuna in the ocean now, because we've curbed fishing, that the orcas are just getting bored.
• 36:09
  And so with their time, they're doing this.
• 36:11
  I don't buy that.
• 36:12
  I think that's a fisherman lobby.
• 36:14
  Like paying some special interest group to be like, hey, we have too much fish.
• 36:19
  And so we can just like start fishing again.
• 36:21
  Another one that I thought was pretty likely was that like they've recognized that boats are fishermen boats.
• 36:26
  And the same reason why you go after the shark is why you go after the boat.
• 36:29
  It's a competing predator that's like depleting my resources.
• 36:32
  That makes sense.
• 36:32
  Like I was going up against a school of tuna and now half of them are gone.
• 36:36
  And there was a boat right there.
• 36:37
  Yeah.
• 36:37
  So like screw that guy.
• 36:38
  Yeah.
• 36:38
  Yeah, exactly.
• 36:40
  That makes a lot of sense.
• 36:41
  That makes a lot more sense to me.
• 36:43
  But I just think like these kinds of stories are so interesting at,
• 36:50
  And so capable of sort of putting humans in their place when it comes to uniqueness.
• 36:58
  Yeah.
• 36:58
  Like we always think that like, oh, culture is very unique to us, right?
• 37:02
  Cognition.
• 37:03
  Cognition is not unique.
• 37:04
  Yep.
• 37:06
  More and more, I mean, I think it's pretty obvious that consciousness is not unique to humans.
• 37:10
  I think a lot of people out there agree.
• 37:12
  And now culture is not unique, right?
• 37:15
  Right.
• 37:15
  Feelings are not unique.
• 37:16
  We can see animals in pain.
• 37:17
  Right.
• 37:18
  Right.
• 37:18
  It's, yeah, it's quite humbling that like, and we've got now scientific backing.
• 37:23
  Yeah, right, right.
• 37:24
  For that.
• 37:24
  I mean, this is the whole, there's a whole sort of culture of no longer eating like calamari.
• 37:31
  Because it's like the idea is like you have this highly intelligent conscious.
• 37:34
  Yeah.
• 37:34
  Yeah.
• 37:35
  creature yeah and there's now this like ethical sort of piece to this like well like you know we don't eat other humans yeah yeah generally speaking generally speaking yeah um i mean dude it's it's interesting to me i mean i i'm i'm a selfish person so i will eat the calamari because it tastes so good but at the same time i will acknowledge that like yeah it's there's it's iffy here and no it's it but this is i think what's interesting is like we're now
• 38:03
  getting more research studies, particularly because we also, like, again, have, like, this multimodal
• 38:10
  systems that are able to sort of track in ways where we get a more robust data set where we can now draw more deeper conclusions because of that.
• 38:18
  And it's, it's not surprising to anyone who watched free Willy that orcas are incredibly intelligent.
• 38:25
  That's exactly right.
• 38:26
  Potentially have culture.
• 38:27
  It's why, or if you've seen that documentary about the sea world or that's just like grueling, I haven't visited a single aquatic theme park after watching that.
• 38:39
  You know how they're solving... That was messed up.
• 38:40
  You know how they're solving their... Because they're clearly struggling for business.
• 38:45
  But the SeaWorld or whatever the brand is in San Diego, this summer they had a summer series of concerts with like...
• 38:57
  Bow Wow and Lil' Camp, like all these OG.
• 39:01
  Okay.
• 39:01
  So you have like these rappers like on the stage that's on the water, like at SeaWorld.
• 39:07
  Oh, God.
• 39:08
  Sold out.
• 39:09
  Yeah.
• 39:10
  On Waka Faka Flame.
• 39:11
  Like it was –
• 39:13
  It did look quite fun.
• 39:14
  And so, look, if you want to change the venue from holding animals in captivity to a concert venue.
• 39:19
  Oh, yeah.
• 39:19
  Then, okay, I thought they were doing it with the animals.
• 39:22
  No, no, no, no, no.
• 39:23
  I was like, geez.
• 39:24
  Waka Flocka.
• 39:25
  I know you got.
• 39:26
  You got Orca flipping over.
• 39:27
  Yeah.
• 39:28
  Because they have no, they're not getting people to come for the other stuff.
• 39:31
  Do that.
• 39:31
  I'll come to those.
• 39:32
  I think that's a great off-ramp.
• 39:34
  Yeah.
• 39:34
  We would love to see more summer series and converting these places into concert venues.
• 39:38
  Fantastic.
• 39:40
  Story number two.
• 39:40
  That was a great one.
• 39:41
  Yeah, it was cool.
• 39:42
  Frontiers in Marine Science.
• 39:45
  We're moving on to our third story, which is about... Shh.
• 39:49
  civet shitted coffee yeah no literally literally shit out coffee if you go to starbucks and you think inflation has made your coffee expensive now there's 80 dollar cups of coffee yeah it's coffee it's shit and no literally i don't know about the taste but it's literally shit coffee that's coming from the shit of something called a civet this is out of the university of kerala and it was in nature scientific reports
• 40:16
  Um, I, when you put this in the show notes, the like title was, you know, Kopi Luwak, the paradoxical luxury of biological processing.
• 40:24
  Yeah.
• 40:25
  And I had not yet.
• 40:27
  Yeah, no.
• 40:27
  And then I had a footnote.
• 40:29
  Civit shitted coffee.
• 40:30
  And they're like, is that shitted?
• 40:31
  Like, like shit.
• 40:32
  And I was like, no, no.
• 40:33
  Like the biological term.
• 40:37
  So tell me about, I mean, so I think I've literally seen this on some billionaire podcast about how they've moved to Civit coffee.
• 40:43
  Cause it's.
• 40:44
  Except yeah, apparently it's, uh, apparently it's exceptional.
• 40:47
  Um, it's a bizarre luxury drink costs 30 to $75 a cup.
• 40:49
  Um,
• 40:53
  1300 per pound you see the you see the coffee and it's all agglomerated you know in a little clump that's because it came out of of the of the anus of a civet okay i'm sorry to our uh listeners but i really i saw this and i was like we try our best to be a pg-13 rated yeah but this is biological it's literal it's literal i'm not i'm not
• 41:18
  I'm not comparing something to shit.
• 41:21
  It's literal.
• 41:22
  Yeah.
• 41:23
  It's literal feces.
• 41:24
  It's literal feces.
• 41:25
  Okay.
• 41:25
  All right.
• 41:26
  All right.
• 41:26
  Let's get serious.
• 41:27
  All right.
• 41:27
  So the market value is $7 billion for this.
• 41:31
  Civic Coffee, the real chemistry behind this bizarre luxury drink.
• 41:35
  And that's what this paper is about.
• 41:36
  Right.
• 41:36
  It's about describing the chemistry behind why when coffee beans go through the gastrointestinal tract...
• 41:44
  of this animal, they taste better when they come out of the other side.
• 41:49
  Okay.
• 41:49
  It's a, it's, it's a coffee called kopi luwak.
• 41:53
  It began in the 18th century because classic colonialism.
• 41:56
  Okay.
• 41:57
  The Dutch East Indies, which is now Indonesia, they had coffee plantation workers.
• 42:02
  The plantation workers were forbidden from drinking the coffee that they were collecting.
• 42:06
  Okay.
• 42:07
  Okay.
• 42:07
  Okay.
• 42:08
  Because, you know, white people.
• 42:10
  Yeah.
• 42:11
  So instead they collected the undigested beans from the droppings of Asian palm civets to drink those.
• 42:20
  And then when the white plantation owners found out and they tried some, they're like, oh, this actually tastes really good.
• 42:27
  And it became a new expensive export for the Dutch because the Dutch back then were always after getting rich.
• 42:33
  This is so funny because there's so many things in like oxtail being $30 a pound or whatever now.
• 42:40
  Kale salads being $20.
• 42:42
  Oxtail and kale were eaten because it was cheap and it was the stuff no one else wanted.
• 42:47
  And now it's been repressed.
• 42:48
  Yeah, dude, I think it's the same with like IPAs.
• 42:51
  Honestly, there's no one who actually enjoys IPAs.
• 42:55
  All right?
• 42:55
  No one can...
• 42:56
  convince me in a blind tasting that they like IPAs.
• 43:00
  Okay.
• 43:00
  But they need everyone else to know that they drink.
• 43:03
  Anyways.
• 43:04
  So I first actually heard about it in the bucket list movie with Jack Nicholson and Morgan Freeman.
• 43:10
  This is one of the things that, that he wanted to drink before he died of cancer.
• 43:16
  So the civet is this Asian common palm civet.
• 43:21
  It looks kind of like a raccoon to me.
• 43:22
  It looks like a hyena mixed with a raccoon.
• 43:25
  Yeah.
• 43:26
  And it consumes the ripest berries, and the beans pass through in about 12 hours, and they become 10 times the value somehow.
• 43:36
  And the digestive process, it's thought that the digestive process modifies the beans' chemical composition, refining flavor precursors, and giving a je ne sais quoi.
• 43:47
  And they're another awesome civet dropping.
• 43:50
  This is funny that this episode we did our first story on biosynthesis.
• 43:53
  Yeah, this is another kind of biosynthesis.
• 43:56
  Exactly.
• 43:56
  Yeah.
• 43:57
  Yeah.
• 43:58
  And, you know, it's actually led, jokes aside, it's led to a lot of unethical cage farming of these civets.
• 44:04
  Because obviously, if you have something that is a commodity that is highly valuable and it comes from an animal, humans don't care about the animal's welfare.
• 44:15
  And so it's led to, like, cage farming driven by the demand.
• 44:19
  So there's demand in Japan, South Korea, and USA.
• 44:23
  And the cage farming has happened in places like Vietnam, Vietnam.
• 44:27
  and those Southeast Asian countries.
• 44:29
  So it has become a real problem.
• 44:31
  I mean, this is literally what we just talked about, about the ethical issue of doing biosynthesis with bacteria.
• 44:35
  Yeah.
• 44:36
  But this is now like you're scaling it up, and it's the same problem set.
• 44:39
  Yeah, yeah, it's the same problem set, right?
• 44:41
  And this particular paper...
• 44:45
  Focused on wild civets, wild civets, not in captivity.
• 44:48
  Okay.
• 44:48
  In the Western Ghats, which are these mountain ranges on the western side of the Indian Peninsula.
• 44:55
  Beautiful, absolutely beautiful.
• 44:56
  One day we will go.
• 44:57
  We will go.
• 44:58
  With our wives and whoever else.
• 45:01
  And it's in Nature Scientific Reports.
• 45:04
  It's wild, meaning that there's coffee plantations there, but then there's wild civets that go and eat some of the berries.
• 45:10
  And you can collect the droppings.
• 45:13
  Yep.
• 45:14
  And what they wanted to figure out was what actually is going on here chemically.
• 45:20
  Right.
• 45:20
  Okay.
• 45:20
  Is there anything going on or is it just placebo?
• 45:23
  Okay.
• 45:23
  So first thing what they did was they actually, you know, what you can do with coffee is you can roast it.
• 45:29
  And when you roast it, you get this thing called a Mylard reaction, which generates that final aroma and taste profile.
• 45:36
  They wanted to look at the coffee bean before the roast, right?
• 45:39
  Because you don't want anything that is volatile or...
• 45:43
  anything that requires some kind of heat.
• 45:45
  You want to look at the raw naked chemical composition, right?
• 45:48
  Before you treat it to, to that heat.
• 45:51
  And so that's exactly what they did.
• 45:53
  They figured out that there's in the civic gut, there's, there's a bioreactor.
• 45:57
  There's just like we have gut bacteria.
• 45:59
  The civets have gut bacteria.
• 46:01
  Yes.
• 46:01
  And,
• 46:02
  We're pretty sure that gluconobacter, which is highly abundant in the civet gut, that's linked to some kind of fermentation.
• 46:09
  It metabolizes like sulfur-containing amino acids.
• 46:13
  It metabolizes hydrogen sulfide.
• 46:15
  So that could be some kind of chemistry that's leading to this unique taste that happens in the civet, right?
• 46:23
  One thing that they did notice was that the beans that came out of the gut of the civet
• 46:30
  were significantly larger than the manually harvested beans.
• 46:33
  Really?
• 46:33
  And that might be because the civet has some natural way of like...
• 46:38
  picking which ones to eat.
• 46:39
  You know what I mean?
• 46:41
  It's not necessarily about what's happening.
• 46:42
  Yeah, yeah, yeah.
• 46:43
  They're selective.
• 46:44
  They're selective about it.
• 46:46
  The other thing they realized was caprylic acid methyl ester and capric acid methyl ester.
• 46:51
  These are two types of medium chain fatty acids.
• 46:55
  Their name is derived from the capric and the caprylic.
• 46:58
  Their name is derived from the Latin term for goat.
• 47:02
  or capra and they're basically these chains of carbon compounds that impart a desirable dairy or milk-like aroma oh okay and and the compounds that came out of the civic gut had a higher concentration of this so we're saying civet shit coffee is the goat yes
• 47:23
  Very nice.
• 47:24
  Very nice.
• 47:25
  Apparently it is.
• 47:26
  Yeah.
• 47:27
  So there's two things that can happen with the study.
• 47:30
  One, you can target fraud.
• 47:34
  You can test for these compounds and be like, this is not.
• 47:36
  This did not come out of a civet sass.
• 47:38
  So anyone who's branding $80 cups of coffee in New York and LA and San Francisco, that's really just selling off-the-shelf Colombian coffee beans.
• 47:46
  You can actually have like a...
• 47:47
  You can actually have a test.
• 47:49
  The other thing that I'm more excited about is, you know, you do have this ethical crisis with the caging and mistreatment of these animals.
• 47:58
  Yes.
• 47:58
  Well, if you could, like, identify the key microbial players and the chemical output, you could make fake ones of this.
• 48:06
  Just how we make fake diamonds now, so we don't need blood diamonds.
• 48:10
  And you could then sell these for a lot less.
• 48:13
  And...
• 48:14
  Without the ethical dilemma.
• 48:15
  Without caging civics.
• 48:16
  Knowing humans, they'll still pay the premium for the real shit.
• 48:20
  For the real stuff.
• 48:21
  As they say.
• 48:21
  For the real stuff.
• 48:22
  Yeah.
• 48:23
  The literal real shit.
• 48:24
  The literal real shit.
• 48:25
  That's what I mean.
• 48:26
  Yeah.
• 48:27
  But I think it's still pretty cool.
• 48:29
  No, that is.
• 48:30
  So I, again, I think I'd only ever heard of this one time.
• 48:34
  And I was like, I think coffee's terrible.
• 48:35
  I mean, I was from a colonial country, so tea.
• 48:37
  Yeah.
• 48:38
  Is my thing.
• 48:40
  A little chai tea.
• 48:42
  I'll take a little Earl Grey, a little oolong.
• 48:44
  However, I understand coffee is what most people like to drink as their...
• 48:53
  hot drink that's right or their cold drink yeah uh a nice iced chai tea latte is is quite nice but this this is this is it's good to know because i will never be buying a cup of civet yeah i don't think it's needed um but we now have like the path yeah move from this maybe if they make the fake one i'll get it i'll get it right right and and also being able to call out frauds uh funny great great heartwarming story i thought that was funny
• 49:20
  Just make sure.
• 49:21
  This is out of nature, scientific reports.
• 49:23
  Look, look.
• 49:23
  And they were like, yeah, this is pretty good.
• 49:25
  It's pretty good.
• 49:26
  Pretty good coffee.
• 49:28
  We're going to wrap up with a nice physics-defying story out of our local favorite UCLA.
• 49:37
  We literally can almost not go through one week without the UC system being one of our stories.
• 49:43
  This week it was two of our stories.
• 49:44
  That's right.
• 49:44
  We started with UCSD.
• 49:51
  to see things that really shouldn't be possible yeah and this is out of the astrophysical journal letters um i'm super excited about this story yeah this is this is a pretty pretty crazy story it's out of the ucla astrophysics department along with a bunch of other institutions
• 50:12
  What they've done is hack the telescope and have sharper views into the universe.
• 50:18
  Okay.
• 50:19
  And they hacked it to the point where even I was like, whoa, whoa, whoa.
• 50:22
  Okay.
• 50:23
  They have, they have a line that I'll get, get to in their paper that I was like, what are you talking about?
• 50:26
  Um,
• 50:28
  Okay, so the goal is always higher angular resolution.
• 50:31
  You want to be able to resolve smaller and smaller things.
• 50:34
  The epitome of that is the Event Horizon Telescope, which gave us that beautiful view of the black hole at the center of our Milky Way and the black hole in M86, which is a far, far away galaxy.
• 50:46
  You're looking at a thing that's about the size of a solar system, maybe several solar systems, but it's millions of light years away.
• 50:56
  Right.
• 50:57
  It's insane.
• 50:58
  It's not close.
• 50:58
  Yeah, it's not close.
• 50:59
  It's something like, I mean, I think somebody was saying something like the head of a pin or they're resolving a quarter on the Empire State Building if you're looking from Los Angeles.
• 51:09
  Right.
• 51:10
  Resolution is what that means.
• 51:12
  How fine detail can you resolve something in the night sky?
• 51:15
  And we want to always push this as far as possible.
• 51:18
  Right.
• 51:19
  This is new work that came out of the UCLA Astrophysics Department.
• 51:24
  What they've done is demonstrate sub-diffraction limited astronomical measurement.
• 51:29
  Okay?
• 51:30
  And that key thing is what got me.
• 51:31
  Sub-diffraction limited.
• 51:33
  So what does that mean?
• 51:34
  Okay.
• 51:35
  There is a limit when it comes to the resolution that you can have with light.
• 51:39
  Okay?
• 51:40
  It's dependent on the wavelength, and it's dependent on the size of your aperture.
• 51:44
  Okay.
• 51:44
  It's called the Rayleigh limit.
• 51:45
  If you've got two points of light that are really close together, as they are on the right-hand side, then they're going to look like one point of light, and I won't be able to resolve them differently.
• 51:54
  On the other hand, if I have two points that are a little bit farther apart, then I can resolve them because they're Gaussian peaks, this sort of like envelope of stuff.
• 52:04
  It's not technically Gaussian.
• 52:05
  It's like this other function called the ARI function.
• 52:07
  But in any case, if they don't overlap enough, I can resolve the two as separate things.
• 52:11
  As separate things.
• 52:12
  Right?
• 52:12
  Yes.
• 52:13
  Yes.
• 52:13
  How far apart they need to be an angle.
• 52:16
  It's not actually distance, right?
• 52:17
  Because if something's closer, then they need to be closer away.
• 52:21
  If they're farther away, then they could be really far.
• 52:23
  It's the angle that matters.
• 52:24
  It's like the angle between them, right?
• 52:27
  And how far away they can be in terms of angle is determined by something called the Raleigh criterion, which is what we just saw.
• 52:32
  It's a ratio between the wavelength and the diameter.
• 52:36
  Okay?
• 52:36
  Wavelength divided by diameter times some constant, which has to do with the shape of your aperture.
• 52:42
  Usually it's 1.22 because your apertures are usually round.
• 52:47
  So the bigger the wavelength, the larger this angle has to be.
• 52:51
  And that makes sense, right?
• 52:52
  Because if the wavelength is really large, then, you know, we've looked at large waves that try to go across a rock.
• 53:00
  They get unbothered.
• 53:01
  Because the large waves will just go through.
• 53:03
  The smaller waves are the ones that will bend.
• 53:05
  And that bending is what actually gives us any type of information.
• 53:10
  Also, the larger the diameter of your lens...
• 53:13
  the smaller you can resolve.
• 53:14
  Yes.
• 53:15
  Right?
• 53:15
  That makes sense.
• 53:16
  That's why we have big telescopes.
• 53:17
  That's why I have these giant, massive telescopes.
• 53:19
  Massive telescopes.
• 53:19
  Big, big mirrors and lenses.
• 53:21
  Yeah, yeah, yeah.
• 53:22
  One of the reasons is light gathering, but the other reason is so that we can have a giant sort of diffraction thingy that lets us resolve.
• 53:33
  Yes.
• 53:33
  Okay?
• 53:34
  And the one that they're doing here, they're using the 8.2 meter Subaru telescope in Hawaii.
• 53:40
  That thing...
• 53:43
  You know, if we take the wavelength of light to be around 656 nanometers, the diffraction limit there with the 8.2 meter Subaru telescope is 20 milli arc seconds.
• 53:54
  Okay.
• 53:54
  Okay.
• 53:54
  And to think about arc seconds, it's like you got degrees.
• 53:57
  From degrees, then you get minutes.
• 53:59
  From minutes, you get seconds.
• 54:00
  And then from seconds is divided into 1,000 arc seconds.
• 54:04
  So it goes 60, 60, then 1,000.
• 54:06
  Okay.
• 54:06
  It's extremely small.
• 54:07
  It means if we were to look at Pluto right now from Earth.
• 54:10
  Yes.
• 54:11
  Yes.
• 54:13
  Pluto would be a fifth the size of that angle.
• 54:15
  So Subaru telescope could actually tell Pluto and Karen apart.
• 54:20
  The two, Pluto and its moon apart, right?
• 54:23
  That's quite nice.
• 54:24
  Because its resolution is smaller than Pluto's.
• 54:28
  That's quite nice.
• 54:28
  Pluto's about 100 arc seconds, and this thing's resolution is 20 milli-arc seconds.
• 54:33
  0.2 times 100, right?
• 54:34
  Yep.
• 54:35
  It's a fifth of that.
• 54:36
  Yep.
• 54:36
  Okay.
• 54:36
  Okay.
• 54:36
  Okay.
• 54:37
  There's a bunch of problems, right?
• 54:38
  The other problem that gets to us is not just Raleigh thing, which is just physics, right?
• 54:44
  Like even the Hubble has this problem.
• 54:46
  James Webb has this problem.
• 54:47
  The other thing is we're under the Earth's atmosphere, and the Earth's atmosphere causes the twinkling of stars.
• 54:52
  It also causes the twinkling of distant...
• 54:55
  lights if you're on top of a mountain right because all the earth's atmosphere has this turbulent packets of warm air and then cold air and highly dense and then lower dense so the light is gonna is gonna start moving around before it gets to your detector interacting with the atmosphere on its way to the exactly capturing it with yeah exactly and so and so the resulting disc of your point source let's say if you're trying to look at a star is
• 55:22
  it's going to go up by 500 to 1,000 milli arc seconds.
• 55:27
  It's going to be 25 to 50 times worse than that theoretical limit.
• 55:31
  And so what we can do is we can do something called adaptive optics.
• 55:35
  We can correct by making a fake star.
• 55:38
  Here what you're seeing is the Keck telescope, and there's two lasers pointing out at where the telescope is looking, and it's creating a fake star there.
• 55:46
  Right.
• 55:47
  The trick is the following.
• 55:49
  You've got a fake star that looks like any other star, but we know that it came from our laser, so it should be stationary.
• 55:56
  The star and the stuff that I'm looking at could be moving and it could have dynamics, but I know that the laser that I'm pointing is definitely straight.
• 56:04
  And the laser is going through the same bit of atmosphere that the starlight is coming in.
• 56:09
  So if I can change...
• 56:12
  If I have some kind of deformable mirror, right, that is taking that starlight and right in the middle, I have like little ways in which I can change the shape of that mirror.
• 56:23
  Yes.
• 56:24
  In order to keep that laser stationary, that's going to cancel out the effects of that column of the atmosphere.
• 56:30
  Makes total sense.
• 56:31
  Right.
• 56:31
  I have to say this because I would be remiss if we didn't.
• 56:34
  We're in between photos six and seven on the show notes.
• 56:37
  Yeah.
• 56:38
  And you literally.
• 56:39
  And I literally didn't say, oh, God.
• 56:40
  Yeah.
• 56:42
  I still don't understand that.
• 56:43
  But the point you're saying is because we have a fixed point from the lasers in the sky that we can then measure and basically remove the impacts of the atmosphere on how we're detecting the other stuff.
• 56:54
  Because we have a fixed point that we control.
• 56:56
  Yes.
• 56:56
  That then can remove the noise that comes from the atmosphere.
• 56:58
  Exactly.
• 56:59
  And then we can finally get to that diffraction limit.
• 57:03
  Got it.
• 57:03
  Okay.
• 57:04
  We can finally get to what the telescope is truly capable of, given Raleigh criterion.
• 57:09
  And here what we're seeing is the center of our Milky Way in infrared.
• 57:14
  On the left is without adaptive optics, and on the right is with adaptive optics.
• 57:18
  My goodness.
• 57:19
  You can see the huge change.
• 57:22
  This is how Andrea Ghez won the Nobel Prize.
• 57:24
  This is crazy.
• 57:25
  For listeners who are not watching the video, and I encourage you to go to YouTube because we always have graphics for this.
• 57:30
  On the left, it basically looks like a gradient, a red-orange gradient.
• 57:34
  With blobs.
• 57:35
  It's just a massive blob.
• 57:36
  And maybe 20 blobs.
• 57:37
  There's no discrete shape of any kind.
• 57:39
  It's just like a sauce.
• 57:41
  On the right, you can see very discrete points of light, multiple.
• 57:45
  There's maybe...
• 57:46
  Even in the zoomed-in version, right?
• 57:47
  There's an inset.
• 57:48
  Yeah, there's an inset.
• 57:49
  And you can see in the inset, there's like...
• 57:51
  It's incredibly high-fidelity comparatively.
• 57:54
  Yeah.
• 57:54
  And here, you're looking at something called Sagittarius A star and the stars around that compact object.
• 58:03
  And if we were to make a movie out of this, you'd see all of the stars going around an invisible point.
• 58:10
  Mm-hmm.
• 58:11
  And that invisible point is our supermassive black hole.
• 58:14
  And this is why Andrew Ghez at UCLA won the Nobel Prize.
• 58:18
  UCLA has had an incredible history of imaging techniques.
• 58:24
  They're really good.
• 58:26
  They've got like two floors dedicated in the Knudsen Hall, I think, to just...
• 58:31
  getting imaging to the next level.
• 58:33
  And this is the next sort of thing that they're doing.
• 58:35
  I mean, LA is the home of Hollywood.
• 58:37
  It is, right?
• 58:37
  Yeah.
• 58:40
  So that's pretty funny.
• 58:41
  So you can see now, right?
• 58:43
  Adaptive optics is insane.
• 58:44
  Yes.
• 58:44
  That's a big deal.
• 58:46
  The other thing that we can do is not just adaptive optics.
• 58:49
  We can make the telescope size bigger without actually making the telescope bigger.
• 58:54
  The effective size.
• 58:55
  You've seen this.
• 58:56
  The VLA.
• 58:56
  You've been to the VLA.
• 58:58
  Very large...
• 59:02
  Array.
• 59:02
  Yeah, a very large array in New Mexico.
• 59:04
  A giant 27-mile-long radio telescope made of smaller telescopes that are all coordinated, and they can sense the time in which the light came.
• 59:13
  They can have them interfere.
• 59:14
  And so the light-gathering power is not as much.
• 59:16
  You're not gathering as much light, but you're...
• 59:21
  resolution is limited by how far away your telescopes are right yeah now with the radio it's easier to do because the radio waves are slower yes with visible light it's harder but actually the gold standard right now is in mount wilson it's called the kara array
• 59:36
  It's six one-meter telescopes all along the mountain that you can see over there.
• 59:42
  The baseline is 330 meters, so about three football fields wide.
• 59:47
  It's effectively a visible optical light telescope that is three football fields wide.
• 59:53
  And with that...
• 59:55
  you can achieve an angular resolution of 0.2 micro arc seconds, which is insane because you can now image other stars like this.
• 1:00:04
  Like this is a star called Altair.
• 1:00:07
  It revolves really fast on its axis, which is why it's got this like really big grapefruit bulge on the equator because it's revolving so fast.
• 1:00:16
  And because in the equator, you can also see that it's dimmer.
• 1:00:19
  Yeah, yeah, yeah.
• 1:00:19
  Because like that gas is farther away from the equator compared to the pole.
• 1:00:23
  Yep.
• 1:00:24
  So it's a little bit dimmer.
• 1:00:26
  This is a single star that we're imaging like this.
• 1:00:29
  Yeah, that's incredible.
• 1:00:31
  It's incredible.
• 1:00:32
  We've also, with the Kara Array, we've been able to see sunspots on other stars.
• 1:00:40
  So star spots on other stars with the Kara Array.
• 1:00:42
  And it was really cool because the last time I went up there was for one of these...
• 1:00:50
  They have...
• 1:00:51
  classical music concerts at Mount Wilson in the telescope dome.
• 1:00:56
  And I, for the longest time thought that nighttime astronomy was dead at Mount Wilson because of the,
• 1:01:03
  light pollution from Los Angeles.
• 1:01:05
  Nighttime astronomy had been retired and all they did was solar astronomy.
• 1:01:08
  But no, CARA was up there.
• 1:01:10
  Six small one-meter telescopes all along the mountain.
• 1:01:13
  They route their light through vacuum tubes.
• 1:01:16
  Which is crazy.
• 1:01:17
  And then they have a central array where they make them interfere and actually get that resolution.
• 1:01:22
  It was cool.
• 1:01:22
  It was cool.
• 1:01:24
  Yeah, that, you know, astronomy, cutting edge astronomy is still alive and well in Mount Wilson.
• 1:01:29
  In beautiful, sunny Los Angeles.
• 1:01:31
  Los Angeles, where it all started.
• 1:01:35
  So this paper's profound claim.
• 1:01:37
  So we've gone through, you know, Raleigh Limit, which is just the physics of light waves.
• 1:01:42
  Yes.
• 1:01:43
  And we've gone through the atmosphere and so on and so forth.
• 1:01:45
  Yes.
• 1:01:46
  Yes.
• 1:01:47
  The core claim that got me thinking about what this paper was, they said, the rally criterion is not fundamental.
• 1:01:55
  So it's like Jack Sparrow being like, it's more like guidelines.
• 1:01:59
  Right?
• 1:02:01
  Like, like I was like, what are you talking about?
• 1:02:04
  Like, I thought the Raleigh criterion, I learned this in undergrad and I thought that was it.
• 1:02:07
  Yeah.
• 1:02:07
  Right.
• 1:02:08
  Right.
• 1:02:08
  Insane.
• 1:02:09
  So here's the thing.
• 1:02:10
  The idea is like there's previously this was viewed as a fundamental limitation.
• 1:02:15
  Yeah.
• 1:02:15
  At a physics level.
• 1:02:16
  At a physics level.
• 1:02:17
  Right.
• 1:02:18
  Then I started reading.
• 1:02:19
  So the Raleigh criterion assumes the following.
• 1:02:22
  The only thing that you're sensitive to is the intensity of the light.
• 1:02:25
  Right.
• 1:02:25
  Okay.
• 1:02:26
  Okay?
• 1:02:27
  Mm-hmm.
• 1:02:27
  You've got a telescope.
• 1:02:28
  You've got a CCD in the back.
• 1:02:30
  The telescope creates an image.
• 1:02:31
  The CCD then...
• 1:02:34
  tells how much intensity of light is coming from this direction, this direction, and it creates an image.
• 1:02:39
  But light is an electromagnetic wave, and there is phase information.
• 1:02:44
  Okay.
• 1:02:45
  Right?
• 1:02:45
  Because the electromagnetic wave is coming at you.
• 1:02:48
  At some point, it's going to be up.
• 1:02:49
  At some point, it's going to be down this way.
• 1:02:51
  Right?
• 1:02:51
  There's a phase.
• 1:02:52
  There's a timing that the light is coming through.
• 1:02:55
  And if you can extract the timing...
• 1:02:57
  then you have both parts of the wave.
• 1:02:59
  You have the amplitude, which is the intensity, which you already had.
• 1:03:01
  Yes.
• 1:03:01
  And you also have the timing in which that wave packet came in.
• 1:03:04
  Yes.
• 1:03:04
  Right?
• 1:03:05
  Yes.
• 1:03:05
  And so now it's kind of like with a complex number, right?
• 1:03:09
  A complex number, as I said the other day, it's a clock that has a length and it has a phase.
• 1:03:13
  Yes.
• 1:03:13
  Yes.
• 1:03:14
  We were only sensitive to the length of this thing with the CCD.
• 1:03:18
  What these guys are doing now is they're getting sensitive to the phase of that light, creating much more information out of that single resolution.
• 1:03:28
  Out of the existing system that's already there.
• 1:03:30
  Yes.
• 1:03:31
  Which is like a key idea here.
• 1:03:32
  They're adding another little piece of equipment.
• 1:03:34
  Right.
• 1:03:34
  Right.
• 1:03:35
  Instead of the CCD, they have this thing that we're going to talk about.
• 1:03:38
  But that thing is now going to capture all of this additional information.
• 1:03:41
  And what's happening is they're dividing up the light into its notes, into its musical notes.
• 1:03:48
  Like when you listen to a musical note, let's say a chord that has A, C, and a G, that's three different piano keys that are put down, right?
• 1:03:57
  How does your ear hear it?
• 1:03:59
  Your ear actually hears the three different notes.
• 1:04:05
  that's in the cochlea.
• 1:04:07
  The cochlea is this curled up little thing.
• 1:04:09
  That's a hardware part of our ear.
• 1:04:11
  And what it does is it's made out of cells of different thickness.
• 1:04:14
  Okay?
• 1:04:16
  The lower frequency, the bass notes, are going to vibrate the cells with the big thickness.
• 1:04:21
  And the higher frequency are going to vibrate the thinner, smaller cells.
• 1:04:25
  So where in this spiral the cells are getting excited tells you which piano notes are being played.
• 1:04:32
  Okay?
• 1:04:33
  Our ear has hardware that is breaking down the sound that is coming in into its respective modes, as you would say.
• 1:04:41
  Into its respective, like...
• 1:04:44
  components.
• 1:04:45
  Okay.
• 1:04:46
  We're going to do the same thing with the light that's coming through a telescope.
• 1:04:49
  Okay.
• 1:04:50
  Okay.
• 1:04:50
  What we have is we've got a wave guide that takes in the light that's coming in from the telescope.
• 1:04:55
  Yes.
• 1:04:55
  Okay.
• 1:04:56
  And then what it's going to do is make it go through this fiber optic sort of chassis.
• 1:05:02
  Okay.
• 1:05:03
  And all of that multimodal, the sound that's coming from an
• 1:05:10
  into each individual fiber optic cable.
• 1:05:12
  Yes.
• 1:05:13
  Okay?
• 1:05:13
  And so you're decomposing the light field into a basis of what are the notes that make up that light field.
• 1:05:20
  Which also changes over time.
• 1:05:22
  Yes.
• 1:05:23
  And each of those notes has a certain phase to it, right?
• 1:05:28
  Because some of the notes are going to be arriving a little bit later, some of them earlier, some of them are going to be louder than the others.
• 1:05:34
  And so if you go to the next one, we'll have...
• 1:05:38
  This is what the light notes look like.
• 1:05:40
  On the left-hand side over here, we've got the actual image.
• 1:05:45
  Let's say it's a dot with a ring around it or something like that.
• 1:05:48
  That can be broken up into a dot in the center.
• 1:05:50
  Then there's maybe two lobes this way.
• 1:05:52
  There's two lobes this way.
• 1:05:53
  And what we can do is we can say, what are the modes, which are these individual notes?
• 1:06:00
  How do they add up?
• 1:06:01
  to make the image that I'm seeing.
• 1:06:03
  That's what that deconstruction is doing.
• 1:06:05
  You know what this looks like?
• 1:06:06
  For any video or film editors that are listening, it looks like the color panel on DaVinci Resolve when you're color correcting your video footage.
• 1:06:16
  It gives you the color wheels of all these different permutations.
• 1:06:19
  It literally looks just like it.
• 1:06:20
  It's actually hilarious.
• 1:06:22
  It's kind of similar in that sense, right?
• 1:06:23
  It's like it's extracting this like low...
• 1:06:28
  information, right?
• 1:06:30
  All the stuff that makes it up.
• 1:06:31
  This is only really possible if you look at very simple objects, for example, a single star, or like stuff around a single star.
• 1:06:37
  If you wanted to image the Eagle Nebula or something, this wouldn't really work.
• 1:06:43
  Mathematics is simple because the source is simple.
• 1:06:45
  That makes sense.
• 1:06:45
  And because the source is simple, I only need to worry about the first, like, 20 notes.
• 1:06:50
  I got you.
• 1:06:50
  I don't need to worry about... Because you can...
• 1:06:52
  I mean, in theory, you can recreate any image using, like, just a bunch of, like, notes like this.
• 1:06:57
  Yes.
• 1:06:57
  But in practice, it's going to get difficult.
• 1:06:59
  That makes sense.
• 1:06:59
  With a simple...
• 1:07:01
  objective, which is just, we're looking at a star, we want to resolve the stuff in the star and around the star very nicely.
• 1:07:07
  That's what we can do.
• 1:07:08
  And the key principle is the phase information of that input is now converted into a measurable intensity difference between all of these different notes, right?
• 1:07:16
  If the star's position, for example, it shifts
• 1:07:19
  then one of those fiber optic inputs is going to be brighter than the other.
• 1:07:23
  If it ships the other way, then another fiber optic input is going to be brighter.
• 1:07:26
  Right?
• 1:07:27
  Yes.
• 1:07:27
  And the raw data has gone from being a CCD to now, it's not a picture of the star, but it's 38 different spectra in each of these different modes.
• 1:07:40
  Okay?
• 1:07:40
  So it's like how...
• 1:07:42
  How loud was this note at this frequency?
• 1:07:45
  Yes.
• 1:07:46
  Yes.
• 1:07:46
  So to speak.
• 1:07:47
  So to speak.
• 1:07:47
  Right?
• 1:07:48
  Right.
• 1:07:48
  This is...
• 1:07:50
  But that's the best way that I can describe it.
• 1:07:52
  We're trying to distill it into an analogy that doesn't require us to go through more layers.
• 1:07:57
  Yeah, go through all of the data.
• 1:07:58
  But it's like...
• 1:07:59
  They've basically got 19 different notes and two polarizations because polarization is also important, right?
• 1:08:05
  The electric field oscillating this way or this way tells us something about what the thing is that is producing that light.
• 1:08:11
  So it's 19 different nodes all in two different polarizations to get 38 different spectra.
• 1:08:17
  The idea is before we were looking at just like one box, and now we have like 19 boxes with two flavors to analyze the same object we were looking at before that was just one box.
• 1:08:30
  Yes, exactly.
• 1:08:31
  And the one box, sure, it had the direct information of how bright this thing was.
• 1:08:36
  But now we're just sophisticating it, right?
• 1:08:38
  We're no longer taking, let's say, just how loud the sound is.
• 1:08:42
  But what are the...
• 1:08:44
  Thingies on top.
• 1:08:45
  The another analogy this makes me think of is like for DJs, when you get a track, right?
• 1:08:51
  The track is like one audio waveform and it has the drums, it has the vocals, it has everything as one waveform.
• 1:08:57
  Exactly.
• 1:08:58
  Now there's all these AI tools that allow you to separate out the drums from the vocals, from the synths.
• 1:09:04
  And now you have it as these, and like there is information when you're looking at just the individual instrument that is hard to decipher when you're looking at the singers.
• 1:09:13
  way.
• 1:09:13
  Yeah, yeah, yeah.
• 1:09:14
  And this is very similar to that.
• 1:09:15
  Yeah, yeah, yeah.
• 1:09:16
  I think that's a good analogy.
• 1:09:18
  Right?
• 1:09:19
  It's taking the aggregate light and it's decomposing it into these individual modes.
• 1:09:24
  You know?
• 1:09:26
  And what you can do now, the second thing that you can do is this star, it's
• 1:09:34
  It's got two different...
• 1:09:35
  So this star specifically, what they're looking at is a star in Canis Minoris, Beta CMI.
• 1:09:42
  It's 162 light years away.
• 1:09:44
  It's surrounded by gas of hydrogen.
• 1:09:46
  Canis Minoris is one of the two dogs that are the hunting companions of Orion, the hunter.
• 1:09:53
  And so...
• 1:09:55
  This star specifically, it's got a gas of hydrogen around it, and that gas is spinning so fast that we can see a Doppler shift.
• 1:10:02
  We're on one side, it's blue shifted because it's coming at us, and on the other side, it's red shifted because it's going away.
• 1:10:10
  So what we want to do is resolve this gas cloud.
• 1:10:13
  There's two ways to do this.
• 1:10:15
  There's two steps.
• 1:10:16
  The first is to say, okay, all of the light, most of the light is coming from the star, right?
• 1:10:21
  There's going to be some jitter,
• 1:10:22
  Because of the adaptive optics.
• 1:10:25
  Remember when we talked about the episode about AI being used for LIGO, for the gravitational observatory?
• 1:10:34
  And I told you that when you have these control systems, you can eliminate low frequency noise, but you're going to inject high frequency noise.
• 1:10:42
  This thing is injecting high frequency noise, right?
• 1:10:44
  So that there's going to be some jitter.
• 1:10:47
  of the star there, right?
• 1:10:48
  What you can do is you can capture that response map of a single star that's basically telling you how the instrument and this modal system that we have is responding to that jitter at every single point that the star is at, okay?
• 1:11:05
  So you've got a response map effectively being like, this is what the astronomical jitter...
• 1:11:11
  From the stars.
• 1:11:12
  Yes.
• 1:11:13
  Yes.
• 1:11:14
  That's from all of the light.
• 1:11:15
  Yes.
• 1:11:16
  Then what you do is you look at something called the H-alpha emission line.
• 1:11:19
  Okay.
• 1:11:20
  The H-alpha emission line is because of quantum mechanics.
• 1:11:23
  Hydrogen has discrete levels.
• 1:11:25
  Yes.
• 1:11:25
  Right?
• 1:11:26
  And whenever the hydrogen atom goes from the N equals 3 to N equals 2 energy, it releases this red light.
• 1:11:37
  That's right at 650 nanometers, about.
• 1:11:40
  I think 656 nanometers, okay?
• 1:11:43
  So if we look only at that frequency of light, we're going to see the gas cloud, the accretion, the disk of hydrogen around the star.
• 1:11:55
  So what we can do is we can take an image in the H-alpha line.
• 1:11:59
  We can take an image with all the light, subtract the two, and we'll get a nice...
• 1:12:05
  Hydrogen gas cloud.
• 1:12:07
  That is surrounding the star.
• 1:12:09
  And so it's like not just that.
• 1:12:11
  Okay.
• 1:12:11
  We're now able to basically make the star's structure and the gas cloud structure discrete using this methodology.
• 1:12:19
  Yes, exactly.
• 1:12:20
  And we can do it because this is sub-diffraction, right?
• 1:12:23
  Right.
• 1:12:23
  This is where we're going way inside what Raleigh even thought was possible.
• 1:12:28
  Yes.
• 1:12:28
  Okay.
• 1:12:29
  Yes.
• 1:12:29
  That tracks us.
• 1:12:30
  It's pretty cool.
• 1:12:31
  And what they found is with this technique, they found that the disc was lopsided.
• 1:12:37
  It's not completely like a plate.
• 1:12:40
  It's not symmetric.
• 1:12:41
  There's like a little bulge to it that they actually saw.
• 1:12:44
  And there's a non-zero shift
• 1:12:47
  in that accretion disk.
• 1:12:48
  And that means that now the modelers can go and be like, okay, how do we, how do we get something like this?
• 1:12:52
  Right.
• 1:12:52
  What is causing that non-zero shift?
• 1:12:54
  Exactly.
• 1:12:56
  Yeah.
• 1:12:56
  I mean, I think, I think it's really cool.
• 1:12:58
  It proves that, you know, you've got this compact, it's cost effective.
• 1:13:01
  You just like stick it in the back of the telescope.
• 1:13:03
  Right.
• 1:13:04
  Right.
• 1:13:04
  Where the light was coming in.
• 1:13:05
  Now, now it goes through this other thing.
• 1:13:07
  It's a bolt on.
• 1:13:08
  It's like a bolt on.
• 1:13:09
  Yeah.
• 1:13:09
  Like you don't have to rebuild any of these systems from scratch.
• 1:13:13
  You can add this as a, as a, as an add on.
• 1:13:17
  And it just receives the existing feed and then processes it.
• 1:13:20
  Yeah, and then processes it in its own way.
• 1:13:23
  And there's so much more that we can do with this.
• 1:13:25
  You can put this on a space telescope, for example.
• 1:13:27
  Because those guys are also limited by Raleigh, but maybe not anymore.
• 1:13:31
  And the last photo that we had, photo 19, I just wanted to...
• 1:13:34
  Yes.
• 1:13:34
  Dwell on a little bit there.
• 1:13:36
  You can see the gas cloud and you can see the clear Doppler.
• 1:13:39
  Yes.
• 1:13:39
  Red, blue shift.
• 1:13:40
  Red, blue shift, right?
• 1:13:41
  Between the part that's coming away from us and the part that's coming towards us.
• 1:13:44
  The blue is where it's coming from towards us.
• 1:13:47
  And the red is where it's going away.
• 1:13:49
  And that scale bar is one micro arc second.
• 1:13:53
  Arc second.
• 1:13:54
  Right.
• 1:13:54
  And I told you before there used to be 20.
• 1:13:56
  Now you've resolved stuff to that.
• 1:13:59
  To one.
• 1:14:00
  And you can even see the lopsidedness that you were talking about earlier in this too.
• 1:14:04
  This is unbelievable.
• 1:14:06
  So I think it's a new dawn of higher than Raleigh resolution.
• 1:14:11
  Yeah.
• 1:14:12
  Yeah.
• 1:14:13
  Which is, I mean, I think it's going to be really, really cool.
• 1:14:16
  Again, this is where we talk about this all the time.
• 1:14:18
  There's a compounding happening right now of these fundamental either techniques, methodologies, tools, combination of these things that are now not only allowing us to
• 1:14:30
  take our existing tooling and make it better, take our existing data sets and analyze it differently.
• 1:14:36
  Yeah.
• 1:14:36
  By giving us a totally new way in which we see the world around us.
• 1:14:39
  Yeah.
• 1:14:40
  This was, this was a good story.
• 1:14:42
  It's really cool.
• 1:14:42
  Now that, that's, this is really cool.
• 1:14:44
  We covered, this was a super episode.
• 1:14:46
  We hit four stories.
• 1:14:47
  Yep.
• 1:14:47
  Uh,
• 1:14:48
  I don't think we're going to hit two hours this episode.
• 1:14:51
  We went through it quick.
• 1:14:52
  We started with UCSD.
• 1:14:53
  We're mass producing camouflage from octopus, octopi.
• 1:14:57
  Yeah.
• 1:14:58
  This was out of UCSD.
• 1:14:59
  By hijacking bacteria.
• 1:15:00
  By hijacking the factory, the biosynthesis.
• 1:15:03
  We're now telling these organisms, you got to go through the factory to make what we want before you can make what you want.
• 1:15:08
  Yeah.
• 1:15:09
  Which is crazy.
• 1:15:10
  Crazy.
• 1:15:10
  Crazy.
• 1:15:12
  This is not an ethics podcast.
• 1:15:14
  That was in Nature Biotechnology.
• 1:15:18
  Our second story was about these new hunting techniques in orcas, both out of CSU Monterey Bay and the Protección Conservación Pelagasia Asociación Civil AC.
• 1:15:27
  That was in Frontiers of Marine Science.
• 1:15:29
  That was a cool story because there's been a lot of news about the yachts being crushed by all of the orcas, but understanding culture might not be specific to human beings.
• 1:15:41
  you know this cross pod communication process happening yeah our third story was about civet coffee and nature nature scientific reports out of the university of carla um that was that was hilarious that was fun i'm never buying 80 coffee no i barely buy nine dollar coffee which is what it is now in la yeah yeah
• 1:16:01
  And we ended with our second UC story of the day out of UCLA.
• 1:16:06
  We are now able to take our telescopes and see more and deeper into the universe in a way we have not before.
• 1:16:14
  The imagery difference that we showed was crazy.
• 1:16:17
  Because it's like, obviously, you don't have to be someone who knows.
• 1:16:20
  You can look at one and look at the other and be like, that one's better.
• 1:16:22
  Oh, wow, that's crazy.
• 1:16:23
  That's better.
• 1:16:23
  Yeah, that's way better.
• 1:16:24
  That's really far away.
• 1:16:25
  And, okay, we can see it that well.
• 1:16:26
  Yeah.
• 1:16:27
  I know when I'm trying to zoom in on my iPhone and I see all the Samsung Galaxy commercials about how you can go zoom into a skyscraper.
• 1:16:34
  Yeah, yeah, yeah.
• 1:16:35
  Fantastic episode.
• 1:16:37
  As always, we appreciate you guys joining us.
• 1:16:39
  Please, if you've made it this far, there's very few of you, but if you have made it this far, I want you to comment either Civit or...
• 1:16:49
  Wagwan or We Love You Krishna in the comments.
• 1:16:53
  So we know you've made it this far.
• 1:16:55
  Please subscribe.
• 1:16:56
  Share it with a friend.
• 1:16:57
  We're trying to really push science.
• 1:16:59
  The shutdown is over.
• 1:17:00
  Yes.
• 1:17:00
  Which might mean we'll get some scientific funding back in the mix.
• 1:17:04
  Yes.
• 1:17:05
  We still need to do more.
• 1:17:06
  Yeah.
• 1:17:07
  We cannot fall behind.
• 1:17:08
  Always.
• 1:17:10
  I'm joined, as always, by my co-host and our resident PhD, Krishna Chowdhury.
• 1:17:15
  My name is Lester Nare.
• 1:17:16
  This is From First Principles.
• 1:17:18
  We'll see you guys next week.